Dangerous Prediction in Roads by Using Machine Learning Models

Many vulnerable, heinous acts that are coming about in the society especially at Roads, most specifically affecting women in the society, are more in recent days. Though new technologies are developing day by day, the fatality rate is not in control to date. Without proper guidance to the people about the particular place where there is a big scope of occurrence of a greater number of accidents, this menace cannot be regulated. It is required to highlight the District-wise data and Roads where the accidents and fatalities are more. The data would help the policymakers to put in place Focused Initiatives regarding those top dangerous roads to address the menace of rising road accidents and resultant fatalities. In this, we created a dataset in Andhra Pradesh where we include those attributes that are helpful for our analysis to predict which road is the most dangerous one. We applied various Machine Learning models such as Logistic regression, Random forest classifier, Gradient Boosting Classifier, Gaussian Naive Bayes, Decision Tree Classifier, K- Nearest Neighbour Classifier and SVM to predict the dangerous roads. It is observed that Logistic Regression provides good accuracy with 87.14.